1. Field of the Invention
This invention relates in general to a system and a method for post-production filtering of noise from digitized images. The invention relates in particular to filtering the noise commonly known as "speckle" from medical ultrasonic images.
2. Description of the Related Art
When an object is scanned by some form of radiation, structures within the object that are too small to be resolved may still disperse, reflect, or otherwise interfere with the signal that is returned to the scanning device. When the device then creates an image based on the returned scan signal, this interference, which is noise, often makes the image less clear.
In medical ultrasonic imaging, the ultrasonic beam transmitted into the body is scattered by the microstructure of the tissue. This interference effect is known as "speckle." Speckle causes the image to appear granular, which in turn obscures smaller structures and masks the presence of low-contrast lesions. The problem is analogous to "snow" on a television screen, which reduces the "sharpness" of the TV image.
Although ultrasonic images are corrupted by speckle, they contain a variety of features should be preserved. Existing image filtering methods, however, typically either introduce severe blurring and loss of diagnostically useful free structures or they do not properly suppress the speckle noise composed of low spatial frequencies. In other words, these methods either "smooth" the parts of the image one wishes to keep sharp (the useful signal) along with smoothing out the noise, or they fail to eliminate noise that itself is relatively "smooth."
One known way to try to reduce the speckle is to average multiple images of the same object structure. Using this method, each image is obtained by varying one or more system parameters so that the speckle patterns in the images are decorrelated. This reduces the variance of the speckle and improves the contrast resolution, that is, the ability to distinguish between two regions of different mean gray level. One may also reduce the variance of the speckle by adaptively smoothing the image through a two-dimensional filter.
Several other image filtering methods have been developed for speckle reduction. Most of these known methods include two parts. First, a single quantity is computed from the local statistics of the image; then, this quantity is used as the input to an adaptive filter, which smoothes out the speckle.
These techniques share the same imaging processing standard, which is that, for each of the pictures elements ("pixels") that make up the image, the current pixel (the image point) is updated with respect to certain conditions through a running window (a local region). The size of the window, which is equivalent to the number of pixels allowed to contribute to the computation, is limited because the signal preservation (for example, of edges, fine structures, and the interface between different organs) deteriorates rapidly as the window size increases. However, a small window may not provide a reliable computational result, especially for computing the "moment statistics" which normally form the basis for these known methods. In other words, if too many other neighboring pixels are allowed to influence the assumed "correct" value of any given pixel, then the "unique" information contained in the pixel may be lost or at least "watered down," but if not enough pixels are considered, then one cannot form an estimate of the general statistical properties in the area of the pixel that is reliable enough to allow one to identify and eliminate the speckle.
In "Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images", Ultrasonics, pp. 41-44, 1986, Bamber and Daft describe a method in which the ratio of the local variance and local mean of the image is used to determine the degree of similarity between the image information and reference information. In "Fast image processing systems for evaluating the clinical potential of ultrasound speckle suppression and parametric imaging", SPIE Vol. 1092, Medical Imaging III: Imaging Processing, pp. 33-39, 1989, Bamber et al. describe using a quantity derived from the ratio of the local mean and local standard deviation as the similarity factor.
Using the numerical quantity of the local statistics in a running window (a local region) to trace back the characteristic of the imaging system is, however, limited by at least the following factors: First, the calculated moment statistics (such as the estimated mean, variance, skewness, and kurtosis) from small number of pixels (the image points) may often not converge to the real moment statistics and they typically show large variance (the large perturbation). Second, ultrasound speckle is related to both tissue properties and the system characteristics of the imager; consequently, it is impossible to adequately represent these many properties and characteristics in a single quantity derived from the local statistics of the image.
In "An adaptive weighted median filter for speckle suppression in medical ultrasound images", IEEE Trans. Circuits Syst., Vol. 36, No. 1, pp. 129-135, 1989, Loupas et al. presented a method for suppressing ultrasound speckle by using a nonlinear filter called an adaptive weighted median filter. The weight coefficients used in this filter were, however, once again adapted from the single quantity of the ratio of the local variance to the local mean of the image. This method consequently also suffered from the same difficulty of reliably evaluate the statistics parameters. Moreover, the median operation does not properly suppress the ultrasonic speckle noise composed of low spatial frequencies, and the adaptive weighted median filter does nothing near edges because of a large variance of the data samples in the fixed local region.
One other drawback of most existing speckle-reduction methods is that they are computationally intensive. For example, calculating variances and mean values in local regions of common sizes typically involves several multiplications. This reduces the usefulness of such techniques for high-speed (preferably "real-time") imaging with high resolution (large numbers of pixels).
What is needed is therefore a system and a method that suppresses speckle in ultrasonic images better than is possible using current solutions, that does so in a computationally more efficient way, and that better preserves the fine structures of images.